Biology subjects

Davidson, N. M.

Publications and source records attributed to Davidson, N. M..

15 recordsLinked to original sources

bioRxiv2026

Nailpolish: Reference-free UMI deduplication and consensus read generation

Long-read sequencing captures full-length transcripts but suffers elevated error rates, and PCR duplicates can distort quantification unless accounted for with UMIs. We present Nailpolish, a fast reference-free tool that error-corrects and deduplicates UMI-tagged long reads by generating consensus sequences via partial order alignment, while separating false duplicates arising from UMI collisions. Across eleven Oxford Nanopore Technologies and Pacific Biosciences datasets spanning bulk, single-cell, spatial, and targeted protocols, Nailpolish reduced per-read error rates and outperformed existing deduplication tools.

bioinformatics↗

bioRxiv2026

seqsizzle: decoding complex barcode and adapter architectures in long-read sequencing data

Visualizing and troubleshooting long-read sequencing data is a common task when analyzing novel protocols, but identifying several primer sequences from reads exceeding 2 kb with fuzzy matching is challenging for the human eye. Here, we present seqsizzle, a cross-platform terminal user interface (TUI) based tool for convenient visualization of long-read sequencing data directly from the command-line, applicable to all sequencing technologies that produce FASTQ or FASTA output. seqsizzle allows primer sequences to be matched with user-specified fuzzy matching thresholds and highlighted with configurable colors. Additional styling can be applied to distinguish mismatches and quality scores, allowing convenient quality control of long-read sequencing data. To further aid troubleshooting, seqsizzle includes a built-in $k$-mer enrichment analysis, enabling the detection of unknown primer sequences or unexpected artifacts, which can then be reviewed via manual inspection. seqsizzle is implemented in Rust and is available open-source from https://github.com/ChangqingW/SeqSizzle.

bioinformatics↗

bioRxiv2026

MNT loss in MYC-driven B lymphoma cells enhances apoptosis, inhibits proliferation and increases sensitivity to cancer drugs.

Our previous mouse genetic studies showed that loss of the transcription repressor MNT enhanced apoptosis of premalignant lymphoid cells over-expressing MYC and inhibited lymphoma development. Here, we have explored the consequences of inducing Mnt deletion in fully malignant lymphoma cells. MNT loss provoked apoptosis of p53 wt and, albeit more slowly, p53 mutant E-Myc lymphoma cells, preceded by elevated levels of major BH3-only proteins BIM and PUMA. By inhibiting apoptosis, we showed that MNT loss also impaired cell cycling and increased senescence. E-Myc lymphoma cells depend on the BCL-2 ortholog MCL-1 for survival and expansion and, importantly, MNT loss enhanced their sensitivity to the MCL-1 inhibitor S63845 and to several chemotherapeutic agents. In BCL-2-overexpressing E-Myc lymphoma cells, which model aggressive human Double Hit Lymphomas, MNT loss enhanced sensitivity to the BCL-2 inhibitor ABT-199, even after BAX loss. Furthermore, MNT deletion improved drug responses of two long-established Burkitt Lymphoma cell lines. SIGNIFICANCEThis study establishing the MNT dependency of E-Myc lymphoma cells and demonstrating that MNT loss enhances their sensitivity to apoptosis induced by conventional chemotherapeutics and BH3 mimetic drugs provides strong proof-of-concept for developing MNT inhibitors to improve treatment of MYC-driven blood cancers.

cancer biology↗

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2025

Long-read sequencing-based atlas of tissue-specific expression of Drp1 transcript variants

Dynamin-related protein 1 (Drp1), encoded by DNM1L, is essential for mitochondrial fission, but its functional roles remain unclear due to isoform-specific effects from alternative splicing. Short-read RNA sequencing fails to resolve full-length isoforms involving distant exons, limiting our understanding. Here, we applied targeted long-read sequencing to profile full-length DNM1L transcripts in human left ventricle and iPSC-derived cardiomyocytes, recovering all annotated isoforms with conserved expression patterns and isoforms 1-4 being most abundant. Functional assays revealed that isoform abundance does not predict enzymatic activity. Extending this to six mouse tissues, we identified distinct, tissue-enriched expression profiles. Functional rescue in Drp1-knockout mouse embryonic fibroblasts showed isoform-dependent differences in mitochondrial fission. Isoforms lacking the A-insert (e.g., b and d) robustly rescued fission, while isoforms enriched in brain or muscle showed only partial rescue, suggesting exons 2 and 3 negatively regulate Drp1 activity. Our cross-species atlas integrates long-read transcriptomics with functional validation, revealing how isoform diversity underpins tissue-specific mitochondrial dynamics and physiological roles of Drp1. SummaryUsing long-read sequencing, we mapped full-length DNM1L/Dnm1l isoforms in human and mouse tissues, uncovering tissue-specific expression and isoform-dependent mitochondrial fission activity. This reveals how alternative splicing shapes Drp1 function, with implications for understanding its role in health and disease.

cell biology↗

bioRxiv2025

Towards accurate, reference-free differential expression: A comprehensive evaluation of long-read de novo transcriptome assembly

Long-read RNA sequencing has significantly advanced transcriptomics by enabling the full length of transcripts to be assessed. However, current analysis methods often depend on a high-quality reference genome and gene annotation. Recently, de novo assembly methods have been developed to utilise long-read data in cases where a reference genome is unavailable, such as in non-model organisms. Despite the potential of these tools, there remains a lack of benchmarking and established protocols for optimal reference-free, long-read transcriptome assembly and differential expression analysis. Here, we comprehensively evaluate the current state-of-the-art long-read de novo transcriptome assembly tools, RATTLE, RNA-Bloom2 and isONform, and compare their performance to one of the leading short-read assemblers, Trinity. We assess various metrics, including assembly quality and computational efficiency, across a range of datasets, which include simulated data and spike-in sequin transcripts, where ground truth is known, and real data from human and pea (Pisum sativum) samples, using a reference-based approach to define truth. To represent contemporary analysis scenarios, the datasets cover depths from 6 million to 60 million reads, Oxford Nanopore Technologies (ONT) cDNA, ONT direct RNA and Pacific Biosciences (PacBio) 10x single-cell sequencing. Critically, we assessed the downstream impact of assembly choice on the detection of differential gene and transcript expression. Our results confirm that long reads generate longer assembled transcripts than short-reads for reference-free analysis, though limitations remain compared to reference-guided approaches, and suggest scope for improved accuracy and reduced redundancy. Of the de novo pipelines, RNA-Bloom2, coupled with Corset for transcript clustering, was the best performing in terms of both accuracy and computational efficiency. Our findings offer guidance when selecting the most effective strategy for long-read differential expression analysis, when a high-quality reference genome is unavailable.

bioinformatics↗

bioRxiv2024

IdentifiHR: predicting homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression

BackgroundApproximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in the homologous recombination (HR) DNA repair mechanism. While there are genomic and transcriptomic methods, developed for other cancer types, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR repair status in HGSC specifically. We have built the first HGSC-specific model to predict HR repair status using gene expression. MethodsWe separated The Cancer Genome Atlas (TCGA) cohort of HGSCs (n = 361) into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification, being a score of HRD genomic damage. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were then used to tune and train a penalised logistic regression model. ResultsIdentifiHR is an elastic net penalised logistic regression model that uses the expression of 209 genes to predict HR status in HGSC. These genes capture known regions of HR-specific copy number alteration, which impact gene expression levels, and preserve the genomic damage signal. IdentifiHR has an accuracy of 85% in the TCGA test set and of 91% in an independent cohort of 99 samples, collected from primary tumours before (n = 74/99) and after autopsy (n = 6/99), in addition to ascites (n = 12/99) and normal fallopian tube samples (n = 7/99). Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing gene expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. ConclusionsIdentifiHR is an accurate model to predict HR status in HGSC using gene expression alone, that is available as an R package from https://github.com/DavidsonGroup/IdentifiHR.

cancer biology↗

bioRxiv2024

Single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) to reliably detect isoform usage and coding mutations in targeted genes at a single-cell level

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrated high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA which allows analysis to be combined with existing short-read RNA-seq datasets. In our investigation of BTK and SF3B1 genes in samples from patients with chronic lymphocytic leukaemia (CLL), we detected SF3B1 isoforms and mutations with high sensitivity. Integration with short-read scRNA-seq data revealed significant gene expression differences in SF3B1-mutated CLL cells, though it did not impact the sensitivity of the anti-cancer drug venetoclax. scRaCH-seqs capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

molecular biology↗

bioRxiv2023

Extraction and quantification of lineage-tracing barcodes with NextClone and CloneDetective

SummaryThe study of clonal dynamics has significantly advanced our understanding of cellular heterogeneity and lineage trajectories. With recent developments in lineage-tracing protocols such as ClonMapper or SPLINTR, which combine DNA barcoding with single-cell RNA sequencing (scRNA-seq), biologists can trace the lineage and evolutionary paths of individual clones while simultaneously observing their transcriptomic changes over time. Here, we present NextClone and CloneDetective, an integrated highly scalable Nextflow pipeline and R package for efficient extraction and quantification of clonal barcodes from scRNA-seq data and DNA sequencing data tagged with lineage-tracing barcodes. We applied both NextClone and CloneDetective to data from a barcoded MCF7 cell line and demonstrate their utility for advancing clonal analysis in the era of high-throughput sequencing. Availability and implementationNextClone and CloneDetective are freely available and open-source on github (https://github.com/phipsonlab/NextClone and https://github.com/phipsonlab/CloneDetective). Documentations and tutorials for NextClone and CloneDetective can be found at https://phipsonlab.github.io/NextClone/ and https://phipsonlab.github.io/CloneDetective/respectively.

bioinformatics↗

bioRxiv2023

Flexiplex: A versatile demultiplexer and search tool for omics data

The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks such as identifying cellular barcodes and UMIs in single cell data, and specific genetic variants for genotyping. However, existing tools which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data. To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplexs application on three use cases, identifying cell line specific sequences in Illumina short-read single cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools. Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

bioinformatics↗

bioRxiv2021

ALLSorts: a RNA-Seq classifier for B-Cell Acute Lymphoblastic Leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patients subtype would have clear clinical utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose. Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes. ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/). Key PointsO_LIALLSorts is a gene expression classifier for B-cell acute lymphoblastic leukemia, which predicts 18 distinct genomic subtypes - including those designated by the World Health Organisation (WHO) and provisional entities. C_LIO_LITrained and validated on over 2300 B-ALL samples, representing each subtype and a variety of clinical features. C_LIO_LICorrectly identified subtypes in 91% of cases in a held-out dataset and between 82-93% across a newly combined cohort of paediatric and adult samples. C_LIO_LIALLSorts assigned subtypes to samples with previously unknown driver events. C_LI ALLsorts is an accurate, comprehensive and freely available classification tool that distinguishes subtypes of B-cell acute lymphoblastic leukemia from RNA-sequencing.

bioinformatics↗

bioRxiv2021

JAFFAL: Detecting fusion genes with long read transcriptome sequencing

Massively parallel short read transcriptome sequencing has greatly expanded our knowledge of fusion genes which are drivers of tumor initiation and progression. In cancer, many fusions are also important diagnostic markers and targets for therapy. Long read transcriptome sequencing allows the full length of fusion transcripts to be discovered, however, this data has a high rate of errors and fusion finding algorithms designed for short reads do not work. While numerous fusion finding algorithms now exist for short read RNA sequencing data, there are few methods to detect fusions using third generation or long read sequencing data. Fusion finding in long read sequencing will allow the discovery of the full isoform structure of fusion genes. Here we present JAFFAL, a method to identify fusions from long-read transcriptome sequencing. We validated JAFFAL using simulation, cell line and patient data from Nanopore and PacBio. We show that fusions can be accurately detected in long read data with JAFFAL, providing better accuracy than other long read fusion finders and with similar performance as state-of-the-art methods applied to short read data. By comparing Nanopore transcriptome sequencing protocols we find that numerous chimeric molecules are generated during cDNA library preparation that are absent when RNA is sequenced directly. We demonstrate that JAFFAL enables fusions to be detected at the level of individual cells, when applied to long read single cell sequencing. Moreover, we demonstrate JAFFAL can identify fusions spanning three genes, highlighting the utility of long reads to characterise the transcriptional products of complex structural rearrangements with unprecedented resolution. JAFFAL is open source and available as part of the JAFFA package at https://github.com/Oshlack/JAFFA/wiki.

bioinformatics↗

bioRxiv2020

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Genomic rearrangements can modify gene function by altering transcript sequences, and have been shown to be drivers in both cancer and rare diseases. Although there are now many methods to detect structural variants from Whole Genome Sequencing (WGS), RNA sequencing (RNA-seq) remains under-utilised as a technology for the detection of gene altering structural variants. Calling fusion genes from RNA-seq data is well established, but other transcriptional variants such as fusions with novel sequence, tandem duplications, large insertions and deletions, and novel splicing are difficult to detect using existing approaches. To identify all types of variants in transcriptomes, we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference free approach, which combines de novo assembly of transcripts with differential expression analysis, to identify up-regulated novel variants in a case sample. We validated MINTIE on simulated and real data sets and compared it with eight other approaches for finding novel transcriptional variants. We found MINTIE was able to detect >85% of variants while no other method was able to achieve this. We applied MINTIE to RNA-seq data from a cohort of acute lymphoblastic leukemia (ALL) patient samples and identified several clinically relevant variants, including a recurrent unpartnered fusion involving the tumour suppressor gene RB1, and variants in ALL-associated genes: tandem duplications in IKZF1 and PAX5, and novel splicing in ETV6. We further demonstrate the utility of MINTIE to identify rare disease variants using RNA-seq, including the discovery of an inter-chromosomal translocation in the DMD gene in a patient with muscular dystrophy. We posit that MINTIE will be able to identify new disease variants across a range of cancers and other disease types.

bioinformatics↗

bioRxiv2017

Clinker: visualising fusion genes detected in RNA-seq data

Genomic profiling efforts have revealed a rich diversity of oncogenic fusion genes, and many are emerging as important therapeutic targets. While there are many ways to identify fusion genes from RNA-seq data, visualising these transcripts and their supporting reads remains challenging. Clinker is a bioinformatics tool written in Python, R and Bpipe, that leverages the superTranscript method to visualise fusion genes. We demonstrate the use of Clinker to obtain interpretable visualisations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell Acute Lymphoblastic Leukaemia (B-ALL).\n\nAvailability and ImplementationClinker is freely available from Github https://github.com/Oshlack/Clinker under a MIT License.\n\nContactalicia.oshlack@mcri.edu.au

bioinformatics↗

bioRxiv2017

Necklace: combining reference and assembled transcriptomes for RNA-Seq analysis

BackgroundRNA-Seq analyses can benefit from performing a genome-guided and de novo assembly, in particular for species where the reference genome or the annotation is incomplete. However, tools for integrating assembled transcriptome with reference annotation are lacking.\n\nFindingsNecklace is a software pipeline that runs genome-guided and de novo assembly and combines the resulting transcriptomes with reference genome annotations. Necklace constructs a compact but comprehensive superTranscriptome out of the assembled and reference data. Reads are subsequently aligned and counted in preparation for differential expression testing.\n\nConclusionsNecklace allows a comprehensive transcriptome to be built from a combination of assembled and annotated transcripts which results in a more comprehensive transcriptome for the majority of organisms. In addition RNA-seq data is mapped back to this newly created superTranscript reference to enable differential expression testing with standard methods. Necklace is available from https://github.com/Oshlack/necklace/wiki under GPL 3.0.

bioinformatics↗